The human face provides countless pieces of information, not least through the display of emotions, driving intensified research interest. Common research methods, such as self-reports, often neglect the subtlety of unconscious emotional responses or risk distortion due to subjective evaluations. Although emotions can occur unconsciously, they sometimes trigger the smallest physiological changes in terms of muscle movements in the face. Manually associating specific emotions with such subtle changes proves to be both time-consuming and intricate. Facereader software, a tool that evaluates video recordings to categorize facial expressions based on muscle movements, including basic emotions, receives more attention, with Noldus' FaceReader software being a prominent example. Nevertheless, a comprehensive pre-purchase guide detailing potential use cases and operational prerequisites is conspicuously lacking, and insights into processing generated data outputs remain confined. Addressing this gap, this paper contributes preliminarily to creating an overview of the use and handling of Noldus FaceReader in different research areas so far. Our findings illuminate pivotal factors for experimental scenario decision-making, offering valuable guidance for methodological considerations involving FaceReader. Consequently, a proposed pioneering guideline aims at standardizing FaceReader utilization, fostering accurate data management, and nuanced result interpretation.